{"total":59,"rows":[{"id":"2211.13445","title":"Delving into Out-of-Distribution Detection with Vision-Language Representations","created":"2022-11-24","cited_by_count":216,"area":null},{"id":"2206.13687","title":"POEM: Out-of-Distribution Detection with Posterior Sampling","created":"2022-06-28","cited_by_count":108,"area":null},{"id":"2406.15627","title":"Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph","created":"2024-06-21","cited_by_count":105,"area":null},{"id":"2305.11391","title":"A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation","created":"2023-05-19","cited_by_count":93,"area":null},{"id":"2304.04521","title":"GL-MCM: Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution Detection","created":"2023-04-10","cited_by_count":36,"area":null},{"id":"2305.03236","title":"A Survey on Out-of-Distribution Detection in NLP","created":"2023-05-05","cited_by_count":31,"area":null},{"id":"2305.13282","title":"Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection","created":"2023-05-22","cited_by_count":29,"area":null},{"id":"2105.07452","title":"How is BERT surprised? Layerwise detection of linguistic anomalies","created":"2021-05-16","cited_by_count":24,"area":null},{"id":"2212.09171","title":"Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Data","created":"2022-12-18","cited_by_count":21,"area":null},{"id":"2405.11227","title":"BadActs: A Universal Backdoor Defense in the Activation Space","created":"2024-05-18","cited_by_count":21,"area":null},{"id":"2109.03079","title":"GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation","created":"2021-09-07","cited_by_count":20,"area":null},{"id":"2410.08113","title":"Robust AI-Generated Text Detection by Restricted Embeddings","created":"2024-10-10","cited_by_count":18,"area":null},{"id":"2502.14427","title":"Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models","created":"2025-02-20","cited_by_count":18,"area":null},{"id":"2308.10261","title":"How Good Are LLMs at Out-of-Distribution Detection?","created":"2023-08-20","cited_by_count":17,"area":null},{"id":"2302.09852","title":"Unsupervised Layer-wise Score Aggregation for Textual OOD Detection","created":"2023-02-20","cited_by_count":15,"area":null},{"id":"2310.02832","title":"Out-of-Distribution Detection by Leveraging Between-Layer Transformation Smoothness","created":"2023-10-04","cited_by_count":15,"area":null},{"id":"2203.11396","title":"Towards Textual Out-of-Domain Detection without In-Domain Labels","created":"2022-03-22","cited_by_count":14,"area":null},{"id":"2301.12715","title":"Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection by Distorting Task-Agnostic Features","created":"2023-01-30","cited_by_count":12,"area":null},{"id":"2205.03598","title":"Towards Computationally Feasible Deep Active Learning","created":"2022-05-07","cited_by_count":11,"area":null},{"id":"2211.05561","title":"Estimating Soft Labels for Out-of-Domain Intent Detection","created":"2022-11-10","cited_by_count":11,"area":null},{"id":"2404.08679","title":"Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector","created":"2024-04-07","cited_by_count":11,"area":null},{"id":"2308.11295","title":"Uncertainty Estimation of Transformers' Predictions via Topological Analysis of the Attention Matrices","created":"2023-08-22","cited_by_count":8,"area":null},{"id":"2205.13346","title":"Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation","created":"2022-05-26","cited_by_count":7,"area":null},{"id":"2502.19320","title":"Shh, don't say that! Domain Certification in LLMs","created":"2025-02-26","cited_by_count":7,"area":null},{"id":"2510.05782","title":"Mysteries of the Deep: Role of Intermediate Representations in Out of Distribution Detection","created":"2025-10-07","cited_by_count":7,"area":null},{"id":"2210.07485","title":"Holistic Sentence Embeddings for Better Out-of-Distribution Detection","created":"2022-10-14","cited_by_count":6,"area":null},{"id":"2302.02182","title":"Online Reinforcement Learning in Non-Stationary Context-Driven Environments","created":"2023-02-04","cited_by_count":6,"area":null},{"id":"2305.03237","title":"Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts","created":"2023-05-05","cited_by_count":6,"area":null},{"id":"2502.18050","title":"Uncertainty-aware abstention in medical diagnosis based on medical texts","created":"2025-02-25","cited_by_count":6,"area":null},{"id":"2111.13362","title":"Data Invariants to Understand Unsupervised Out-of-Distribution Detection","created":"2021-11-26","cited_by_count":5,"area":null},{"id":"2204.05695","title":"Self-Supervised Losses for One-Class Textual Anomaly Detection","created":"2022-04-12","cited_by_count":5,"area":null},{"id":"2204.07804","title":"Learning to Classify Open Intent via Soft Labeling and Manifold Mixup","created":"2022-04-16","cited_by_count":5,"area":null},{"id":"2307.07002","title":"Classical Out-of-Distribution Detection Methods Benchmark in Text Classification Tasks","created":"2023-07-13","cited_by_count":5,"area":null},{"id":"2404.14415","title":"Domain Adaptation in Intent Classification Systems: A Review","created":"2024-03-26","cited_by_count":4,"area":null},{"id":"2410.13649","title":"A new approach for fine-tuning sentence transformers for intent classification and out-of-scope detection tasks","created":"2024-10-17","cited_by_count":4,"area":null},{"id":"2605.21602","title":"Benchmarking and Improving Monitors for Out-Of-Distribution Alignment Failure in LLMs","created":"2026-05-20","cited_by_count":4,"area":null},{"id":"2211.13527","title":"Beyond Mahalanobis-Based Scores for Textual OOD Detection","created":"2022-11-24","cited_by_count":3,"area":null},{"id":"2305.14696","title":"SELFOOD: Self-Supervised Out-Of-Distribution Detection via Learning to Rank","created":"2023-05-24","cited_by_count":3,"area":null},{"id":"2310.14001","title":"Toward Stronger Textual Attack Detectors","created":"2023-10-21","cited_by_count":3,"area":null},{"id":"2407.13141","title":"Out-of-Distribution Detection through Soft Clustering with Non-Negative Kernel Regression","created":"2024-07-18","cited_by_count":3,"area":null},{"id":"2411.14013","title":"Lightweight Model Attribution and Detection of Synthetic Speech via Audio Residual Fingerprints","created":"2024-11-21","cited_by_count":3,"area":null},{"id":"2606.05725","title":"An Embarrassingly Simple Detector for Model Extraction Attacks in Large Language Model API Traffic","created":"2026-06-04","cited_by_count":3,"area":null},{"id":"2301.11660","title":"Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning","created":"2023-01-27","cited_by_count":2,"area":null},{"id":"2303.04134","title":"A Hybrid Architecture for Out of Domain Intent Detection and Intent Discovery","created":"2023-03-07","cited_by_count":2,"area":null},{"id":"2311.13102","title":"Detecting out-of-distribution text using topological features of transformer-based language models","created":"2023-11-22","cited_by_count":2,"area":null},{"id":"2211.15718","title":"Contrastive Novelty-Augmented Learning: Anticipating Outliers with Large Language Models","created":"2022-11-28","cited_by_count":1,"area":null},{"id":"2307.13069","title":"General-Purpose Multi-Modal OOD Detection Framework","created":"2023-07-24","cited_by_count":1,"area":null},{"id":"2402.12997","title":"Towards Trustworthy Reranking: A Simple yet Effective Abstention Mechanism","created":"2024-02-20","cited_by_count":1,"area":null},{"id":"2406.12915","title":"How Out-of-Distribution Detection Learning Theory Enhances Transformer: Learnability and Reliability","created":"2024-06-13","cited_by_count":1,"area":null},{"id":"2502.20532","title":"Finer Disentanglement of Aleatoric Uncertainty Can Accelerate Chemical Histopathology Imaging","created":"2025-02-27","cited_by_count":1,"area":null},{"id":"2503.15889","title":"LeanTTA: A Backpropagation-Free and Stateless Approach to Quantized Test-Time Adaptation on Edge Devices","created":"2025-03-20","cited_by_count":1,"area":null},{"id":"2205.11887","title":"Accuracy on In-Domain Samples Matters When Building Out-of-Domain detectors: A Reply to Marek et al. (2021)","created":"2022-05-24","cited_by_count":0,"area":null},{"id":"2210.09132","title":"Pseudo-OOD training for robust language models","created":"2022-10-17","cited_by_count":0,"area":null},{"id":"2306.08852","title":"BED: Bi-Encoder-Based Detectors for Out-of-Distribution Detection","created":"2023-06-15","cited_by_count":0,"area":null},{"id":"2404.06217","title":"VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution Detection","created":"2024-04-09","cited_by_count":0,"area":null},{"id":"2407.09011","title":"One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive Learning","created":"2024-07-12","cited_by_count":0,"area":null},{"id":"2603.07330","title":"To Predict or Not to Predict? Towards reliable uncertainty estimation in the presence of noise","created":"2026-03-07","cited_by_count":0,"area":null},{"id":"2609.00961","title":"Few-Shot Out of Domain Intent Detection with Covariance Corrected Mahalanobis Distance","created":"2026-09-01","cited_by_count":0,"area":null},{"id":"2609.18804","title":"Beyond frequency measures: Can contextual embeddings capture meaning change in scientific texts?","created":"2026-09-16","cited_by_count":0,"area":null}],"next_cursor":null}